Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations17182
Missing cells17
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory212.0 B

Variable types

Text1
Numeric13
Categorical4

Alerts

Battery_Active_Power_Set_Response has constant value "0.0" Constant
FC_Active_Power_FC_END_Set has constant value "40.0" Constant
FC_Active_Power_FC_end_Set_Response has constant value "40.0" Constant
GE_Active_Power is highly overall correlated with GE_Body_Active_Power and 2 other fieldsHigh correlation
GE_Body_Active_Power is highly overall correlated with GE_Active_Power and 2 other fieldsHigh correlation
GE_Body_Active_Power_Set_Response is highly overall correlated with GE_Active_Power and 2 other fieldsHigh correlation
Inlet_Temperature_of_Chilled_Water is highly overall correlated with Outlet_TemperatureHigh correlation
Island_mode_MCCB_AC_Voltage is highly overall correlated with MG-LV-MSB_AC_Voltage and 2 other fieldsHigh correlation
Island_mode_MCCB_Active_Power is highly overall correlated with GE_Active_Power and 3 other fieldsHigh correlation
Island_mode_MCCB_Frequency is highly overall correlated with MG-LV-MSB_FrequencyHigh correlation
MG-LV-MSB_AC_Voltage is highly overall correlated with Island_mode_MCCB_AC_Voltage and 2 other fieldsHigh correlation
MG-LV-MSB_Frequency is highly overall correlated with Island_mode_MCCB_FrequencyHigh correlation
Outlet_Temperature is highly overall correlated with Inlet_Temperature_of_Chilled_WaterHigh correlation
PVPCS_Active_Power is highly overall correlated with Island_mode_MCCB_AC_Voltage and 3 other fieldsHigh correlation
Receiving_Point_AC_Voltage is highly overall correlated with Island_mode_MCCB_AC_Voltage and 2 other fieldsHigh correlation
FC_Active_Power is highly imbalanced (68.4%) Imbalance
Timestamp has unique values Unique
Battery_Active_Power has 3892 (22.7%) zeros Zeros
PVPCS_Active_Power has 8700 (50.6%) zeros Zeros

Reproduction

Analysis started2025-03-01 02:44:11.957657
Analysis finished2025-03-01 02:44:49.928241
Duration37.97 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Timestamp
Text

Unique 

Distinct17182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-03-01T02:44:50.309283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.99936
Min length8

Characters and Unicode

Total characters326447
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17182 ?
Unique (%)100.0%

Sample

1st row2023/04/01 00:00:01
2nd row2023/04/01 00:00:11
3rd row2023/04/01 00:00:21
4th row2023/04/01 00:00:31
5th row2023/04/01 00:00:41
ValueCountFrequency (%)
2023/04/01 8640
25.1%
2023/04/02 8541
24.9%
23:42:11 2
 
< 0.1%
23:41:11 2
 
< 0.1%
23:41:21 2
 
< 0.1%
23:41:31 2
 
< 0.1%
23:41:41 2
 
< 0.1%
23:41:51 2
 
< 0.1%
23:42:01 2
 
< 0.1%
23:42:21 2
 
< 0.1%
Other values (8633) 17166
50.0%
2025-03-01T02:44:50.858853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68371
20.9%
2 55312
16.9%
1 42647
13.1%
/ 34364
10.5%
: 34362
10.5%
3 26705
 
8.2%
4 26042
 
8.0%
17181
 
5.3%
5 8839
 
2.7%
7 3156
 
1.0%
Other values (3) 9468
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 326447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 68371
20.9%
2 55312
16.9%
1 42647
13.1%
/ 34364
10.5%
: 34362
10.5%
3 26705
 
8.2%
4 26042
 
8.0%
17181
 
5.3%
5 8839
 
2.7%
7 3156
 
1.0%
Other values (3) 9468
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 326447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 68371
20.9%
2 55312
16.9%
1 42647
13.1%
/ 34364
10.5%
: 34362
10.5%
3 26705
 
8.2%
4 26042
 
8.0%
17181
 
5.3%
5 8839
 
2.7%
7 3156
 
1.0%
Other values (3) 9468
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 326447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 68371
20.9%
2 55312
16.9%
1 42647
13.1%
/ 34364
10.5%
: 34362
10.5%
3 26705
 
8.2%
4 26042
 
8.0%
17181
 
5.3%
5 8839
 
2.7%
7 3156
 
1.0%
Other values (3) 9468
 
2.9%

Battery_Active_Power
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.10261335
Minimum-0.4
Maximum0.2
Zeros3892
Zeros (%)22.7%
Negative11325
Negative (%)65.9%
Memory size134.4 KiB
2025-03-01T02:44:50.973877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile-0.3
Q1-0.2
median-0.1
Q30
95-th percentile0.1
Maximum0.2
Range0.6
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.12152328
Coefficient of variation (CV)-1.1842833
Kurtosis-0.63780945
Mean-0.10261335
Median Absolute Deviation (MAD)0.1
Skewness-0.05201605
Sum-1763
Variance0.014767908
MonotonicityNot monotonic
2025-03-01T02:44:51.094668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.1 5324
31.0%
0 3892
22.7%
-0.2 3843
22.4%
-0.3 1987
 
11.6%
0.1 1903
 
11.1%
-0.4 171
 
1.0%
0.2 61
 
0.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-0.4 171
 
1.0%
-0.3 1987
 
11.6%
-0.2 3843
22.4%
-0.1 5324
31.0%
0 3892
22.7%
0.1 1903
 
11.1%
0.2 61
 
0.4%
ValueCountFrequency (%)
0.2 61
 
0.4%
0.1 1903
 
11.1%
0 3892
22.7%
-0.1 5324
31.0%
-0.2 3843
22.4%
-0.3 1987
 
11.6%
-0.4 171
 
1.0%

Battery_Active_Power_Set_Response
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1006.9 KiB
0.0
17181 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51543
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17181
> 99.9%
(Missing) 1
 
< 0.1%

Length

2025-03-01T02:44:51.272734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T02:44:51.360603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17181
100.0%

Most occurring characters

ValueCountFrequency (%)
0 34362
66.7%
. 17181
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34362
66.7%
. 17181
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34362
66.7%
. 17181
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34362
66.7%
. 17181
33.3%

PVPCS_Active_Power
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.384145
Minimum-1
Maximum47
Zeros8700
Zeros (%)50.6%
Negative116
Negative (%)0.7%
Memory size134.4 KiB
2025-03-01T02:44:51.487246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q330
95-th percentile44
Maximum47
Range48
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.967103
Coefficient of variation (CV)1.2677016
Kurtosis-1.087904
Mean13.384145
Median Absolute Deviation (MAD)0
Skewness0.77509939
Sum229953
Variance287.88258
MonotonicityNot monotonic
2025-03-01T02:44:51.682627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 8700
50.6%
44 848
 
4.9%
43 550
 
3.2%
42 358
 
2.1%
40 291
 
1.7%
45 264
 
1.5%
41 263
 
1.5%
39 240
 
1.4%
38 240
 
1.4%
1 178
 
1.0%
Other values (39) 5249
30.5%
ValueCountFrequency (%)
-1 116
 
0.7%
0 8700
50.6%
1 178
 
1.0%
2 168
 
1.0%
3 106
 
0.6%
4 140
 
0.8%
5 158
 
0.9%
6 147
 
0.9%
7 154
 
0.9%
8 117
 
0.7%
ValueCountFrequency (%)
47 5
 
< 0.1%
46 5
 
< 0.1%
45 264
 
1.5%
44 848
4.9%
43 550
3.2%
42 358
2.1%
41 263
 
1.5%
40 291
 
1.7%
39 240
 
1.4%
38 240
 
1.4%

GE_Body_Active_Power
Real number (ℝ)

High correlation 

Distinct177
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean135.43711
Minimum12
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:52.305527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile108
Q1118
median126
Q3145
95-th percentile200
Maximum227
Range215
Interquartile range (IQR)27

Descriptive statistics

Standard deviation27.624361
Coefficient of variation (CV)0.20396449
Kurtosis1.0689151
Mean135.43711
Median Absolute Deviation (MAD)10
Skewness1.2841774
Sum2326945
Variance763.10531
MonotonicityNot monotonic
2025-03-01T02:44:52.486937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 606
 
3.5%
120 576
 
3.4%
122 564
 
3.3%
119 547
 
3.2%
125 531
 
3.1%
124 522
 
3.0%
126 515
 
3.0%
121 499
 
2.9%
129 486
 
2.8%
127 475
 
2.8%
Other values (167) 11860
69.0%
ValueCountFrequency (%)
12 1
< 0.1%
35 1
< 0.1%
36 1
< 0.1%
41 2
< 0.1%
47 1
< 0.1%
49 1
< 0.1%
50 1
< 0.1%
51 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
ValueCountFrequency (%)
227 1
 
< 0.1%
225 2
 
< 0.1%
224 1
 
< 0.1%
223 2
 
< 0.1%
222 5
 
< 0.1%
221 10
0.1%
220 3
 
< 0.1%
219 15
0.1%
218 13
0.1%
217 12
0.1%

GE_Active_Power
Real number (ℝ)

High correlation 

Distinct728
Distinct (%)4.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean133.19535
Minimum-21.5
Maximum238.8
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)0.1%
Memory size134.4 KiB
2025-03-01T02:44:52.675087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-21.5
5-th percentile94.300003
Q1112
median128.8
Q3146
95-th percentile198.3
Maximum238.8
Range260.3
Interquartile range (IQR)34

Descriptive statistics

Standard deviation30.781072
Coefficient of variation (CV)0.23109719
Kurtosis0.86180118
Mean133.19535
Median Absolute Deviation (MAD)17
Skewness0.66070883
Sum2288429.3
Variance947.47436
MonotonicityNot monotonic
2025-03-01T02:44:52.914534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.300003 115
 
0.7%
123.800003 109
 
0.6%
128.800003 101
 
0.6%
130.300003 100
 
0.6%
133.5 100
 
0.6%
126.300003 99
 
0.6%
125.300003 98
 
0.6%
131 97
 
0.6%
128 97
 
0.6%
126 97
 
0.6%
Other values (718) 16168
94.1%
ValueCountFrequency (%)
-21.5 1
< 0.1%
-19.5 1
< 0.1%
-16.5 1
< 0.1%
-7.7 2
< 0.1%
-4 1
< 0.1%
-3.2 1
< 0.1%
-1 1
< 0.1%
-0.5 1
< 0.1%
-0.2 1
< 0.1%
3.8 1
< 0.1%
ValueCountFrequency (%)
238.800003 1
< 0.1%
238 1
< 0.1%
234.5 1
< 0.1%
233.800003 2
< 0.1%
233.5 1
< 0.1%
232.300003 1
< 0.1%
231.800003 1
< 0.1%
231 1
< 0.1%
230.800003 1
< 0.1%
230.5 1
< 0.1%

GE_Body_Active_Power_Set_Response
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean136.36813
Minimum122
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:53.072035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile122
Q1122
median122
Q3155
95-th percentile200
Maximum200
Range78
Interquartile range (IQR)33

Descriptive statistics

Standard deviation25.954736
Coefficient of variation (CV)0.19032846
Kurtosis1.2857402
Mean136.36813
Median Absolute Deviation (MAD)0
Skewness1.6414227
Sum2342940.8
Variance673.6483
MonotonicityIncreasing
2025-03-01T02:44:53.214597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
122 12401
72.2%
155 2726
 
15.9%
200 1980
 
11.5%
152.5 21
 
0.1%
181.300003 21
 
0.1%
144 11
 
0.1%
134 10
 
0.1%
149 6
 
< 0.1%
127 3
 
< 0.1%
140 2
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
122 12401
72.2%
127 3
 
< 0.1%
134 10
 
0.1%
140 2
 
< 0.1%
144 11
 
0.1%
149 6
 
< 0.1%
152.5 21
 
0.1%
155 2726
 
15.9%
181.300003 21
 
0.1%
200 1980
 
11.5%
ValueCountFrequency (%)
200 1980
 
11.5%
181.300003 21
 
0.1%
155 2726
 
15.9%
152.5 21
 
0.1%
149 6
 
< 0.1%
144 11
 
0.1%
140 2
 
< 0.1%
134 10
 
0.1%
127 3
 
< 0.1%
122 12401
72.2%

FC_Active_Power_FC_END_Set
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1023.7 KiB
40.0
17181 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters68724
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40.0
2nd row40.0
3rd row40.0
4th row40.0
5th row40.0

Common Values

ValueCountFrequency (%)
40.0 17181
> 99.9%
(Missing) 1
 
< 0.1%

Length

2025-03-01T02:44:53.404453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T02:44:53.495763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
40.0 17181
100.0%

Most occurring characters

ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

FC_Active_Power
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1023.7 KiB
38.0
16199 
37.0
 
982

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters68724
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38.0
2nd row38.0
3rd row38.0
4th row38.0
5th row38.0

Common Values

ValueCountFrequency (%)
38.0 16199
94.3%
37.0 982
 
5.7%
(Missing) 1
 
< 0.1%

Length

2025-03-01T02:44:53.604433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T02:44:53.698372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
38.0 16199
94.3%
37.0 982
 
5.7%

Most occurring characters

ValueCountFrequency (%)
3 17181
25.0%
. 17181
25.0%
0 17181
25.0%
8 16199
23.6%
7 982
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 17181
25.0%
. 17181
25.0%
0 17181
25.0%
8 16199
23.6%
7 982
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 17181
25.0%
. 17181
25.0%
0 17181
25.0%
8 16199
23.6%
7 982
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 17181
25.0%
. 17181
25.0%
0 17181
25.0%
8 16199
23.6%
7 982
 
1.4%
Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1023.7 KiB
40.0
17181 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters68724
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40.0
2nd row40.0
3rd row40.0
4th row40.0
5th row40.0

Common Values

ValueCountFrequency (%)
40.0 17181
> 99.9%
(Missing) 1
 
< 0.1%

Length

2025-03-01T02:44:53.811784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T02:44:53.906698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
40.0 17181
100.0%

Most occurring characters

ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34362
50.0%
4 17181
25.0%
. 17181
25.0%

Island_mode_MCCB_Active_Power
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-154.33438
Minimum-232
Maximum-27
Zeros0
Zeros (%)0.0%
Negative17181
Negative (%)> 99.9%
Memory size134.4 KiB
2025-03-01T02:44:54.028636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-232
5-th percentile-208
Q1-176
median-145
Q3-129
95-th percentile-119
Maximum-27
Range205
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.131338
Coefficient of variation (CV)-0.19523413
Kurtosis-0.76051586
Mean-154.33438
Median Absolute Deviation (MAD)20
Skewness-0.4862299
Sum-2651619
Variance907.89755
MonotonicityNot monotonic
2025-03-01T02:44:54.218946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-130 442
 
2.6%
-127 430
 
2.5%
-131 430
 
2.5%
-126 422
 
2.5%
-129 420
 
2.4%
-128 409
 
2.4%
-125 383
 
2.2%
-132 379
 
2.2%
-133 374
 
2.2%
-134 371
 
2.2%
Other values (163) 13121
76.4%
ValueCountFrequency (%)
-232 1
 
< 0.1%
-229 4
 
< 0.1%
-228 2
 
< 0.1%
-227 3
 
< 0.1%
-226 4
 
< 0.1%
-225 12
0.1%
-224 10
0.1%
-223 15
0.1%
-222 14
0.1%
-221 21
0.1%
ValueCountFrequency (%)
-27 1
< 0.1%
-32 1
< 0.1%
-44 1
< 0.1%
-48 1
< 0.1%
-49 1
< 0.1%
-50 1
< 0.1%
-52 1
< 0.1%
-53 1
< 0.1%
-54 1
< 0.1%
-55 2
< 0.1%

MG-LV-MSB_AC_Voltage
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean486.89098
Minimum478
Maximum493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:54.373815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile483
Q1485
median487
Q3489
95-th percentile490
Maximum493
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3660237
Coefficient of variation (CV)0.0048594528
Kurtosis-0.76025754
Mean486.89098
Median Absolute Deviation (MAD)2
Skewness-0.2821708
Sum8365274
Variance5.5980683
MonotonicityNot monotonic
2025-03-01T02:44:54.513854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
489 3558
20.7%
488 2408
14.0%
487 2140
12.5%
486 2039
11.9%
485 1900
11.1%
484 1510
8.8%
490 1271
 
7.4%
483 1110
 
6.5%
482 553
 
3.2%
491 506
 
2.9%
Other values (5) 186
 
1.1%
ValueCountFrequency (%)
478 1
 
< 0.1%
480 1
 
< 0.1%
481 52
 
0.3%
482 553
 
3.2%
483 1110
6.5%
484 1510
8.8%
485 1900
11.1%
486 2039
11.9%
487 2140
12.5%
488 2408
14.0%
ValueCountFrequency (%)
493 8
 
< 0.1%
492 124
 
0.7%
491 506
 
2.9%
490 1271
 
7.4%
489 3558
20.7%
488 2408
14.0%
487 2140
12.5%
486 2039
11.9%
485 1900
11.1%
484 1510
8.8%

Receiving_Point_AC_Voltage
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean484.51621
Minimum475
Maximum491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:54.660276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum475
5-th percentile480
Q1482
median485
Q3487
95-th percentile488
Maximum491
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5288871
Coefficient of variation (CV)0.0052194065
Kurtosis-0.83090238
Mean484.51621
Median Absolute Deviation (MAD)2
Skewness-0.23827579
Sum8324473
Variance6.3952698
MonotonicityNot monotonic
2025-03-01T02:44:54.807796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
487 3019
17.6%
486 2458
14.3%
485 2053
11.9%
484 2009
11.7%
482 1785
10.4%
483 1679
9.8%
481 1327
7.7%
488 1107
 
6.4%
480 939
 
5.5%
489 392
 
2.3%
Other values (5) 413
 
2.4%
ValueCountFrequency (%)
475 1
 
< 0.1%
478 7
 
< 0.1%
479 268
 
1.6%
480 939
 
5.5%
481 1327
7.7%
482 1785
10.4%
483 1679
9.8%
484 2009
11.7%
485 2053
11.9%
486 2458
14.3%
ValueCountFrequency (%)
491 10
 
0.1%
490 127
 
0.7%
489 392
 
2.3%
488 1107
 
6.4%
487 3019
17.6%
486 2458
14.3%
485 2053
11.9%
484 2009
11.7%
483 1679
9.8%
482 1785
10.4%

Island_mode_MCCB_AC_Voltage
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean486.86852
Minimum481
Maximum493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:54.950992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum481
5-th percentile483
Q1485
median487
Q3489
95-th percentile490
Maximum493
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3699464
Coefficient of variation (CV)0.004867734
Kurtosis-0.77399886
Mean486.86852
Median Absolute Deviation (MAD)2
Skewness-0.27639483
Sum8364888
Variance5.6166462
MonotonicityNot monotonic
2025-03-01T02:44:55.115557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
489 3548
20.6%
488 2412
14.0%
487 2123
12.4%
486 2053
11.9%
485 1902
11.1%
484 1510
8.8%
490 1243
 
7.2%
483 1146
 
6.7%
482 560
 
3.3%
491 494
 
2.9%
Other values (3) 190
 
1.1%
ValueCountFrequency (%)
481 60
 
0.3%
482 560
 
3.3%
483 1146
 
6.7%
484 1510
8.8%
485 1902
11.1%
486 2053
11.9%
487 2123
12.4%
488 2412
14.0%
489 3548
20.6%
490 1243
 
7.2%
ValueCountFrequency (%)
493 9
 
0.1%
492 121
 
0.7%
491 494
 
2.9%
490 1243
 
7.2%
489 3548
20.6%
488 2412
14.0%
487 2123
12.4%
486 2053
11.9%
485 1902
11.1%
484 1510
8.8%

Island_mode_MCCB_Frequency
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean59.999576
Minimum59.919998
Maximum60.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:55.261395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum59.919998
5-th percentile59.959999
Q159.990002
median60
Q360.009998
95-th percentile60.029999
Maximum60.07
Range0.150002
Interquartile range (IQR)0.019996

Descriptive statistics

Standard deviation0.020594882
Coefficient of variation (CV)0.00034325046
Kurtosis0.065120624
Mean59.999576
Median Absolute Deviation (MAD)0.009998
Skewness-0.27387404
Sum1030852.7
Variance0.00042414917
MonotonicityNot monotonic
2025-03-01T02:44:55.433359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60 3298
19.2%
60.009998 3205
18.7%
59.990002 2830
16.5%
60.02 2323
13.5%
59.98 1819
10.6%
60.029999 1140
 
6.6%
59.970001 1100
 
6.4%
59.959999 593
 
3.5%
60.040001 350
 
2.0%
59.950001 262
 
1.5%
Other values (6) 261
 
1.5%
ValueCountFrequency (%)
59.919998 5
 
< 0.1%
59.93 20
 
0.1%
59.939999 89
 
0.5%
59.950001 262
 
1.5%
59.959999 593
 
3.5%
59.970001 1100
 
6.4%
59.98 1819
10.6%
59.990002 2830
16.5%
60 3298
19.2%
60.009998 3205
18.7%
ValueCountFrequency (%)
60.07 2
 
< 0.1%
60.060001 25
 
0.1%
60.049999 120
 
0.7%
60.040001 350
 
2.0%
60.029999 1140
 
6.6%
60.02 2323
13.5%
60.009998 3205
18.7%
60 3298
19.2%
59.990002 2830
16.5%
59.98 1819
10.6%

MG-LV-MSB_Frequency
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean59.99953
Minimum59.919998
Maximum60.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:55.585225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum59.919998
5-th percentile59.959999
Q159.990002
median60
Q360.009998
95-th percentile60.029999
Maximum60.07
Range0.150002
Interquartile range (IQR)0.019996

Descriptive statistics

Standard deviation0.020615373
Coefficient of variation (CV)0.00034359224
Kurtosis0.027334928
Mean59.99953
Median Absolute Deviation (MAD)0.009998
Skewness-0.26700286
Sum1030851.9
Variance0.00042499361
MonotonicityNot monotonic
2025-03-01T02:44:55.731381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60 3254
18.9%
60.009998 3150
18.3%
59.990002 2813
16.4%
60.02 2383
13.9%
59.98 1907
11.1%
60.029999 1127
 
6.6%
59.970001 1090
 
6.3%
59.959999 577
 
3.4%
60.040001 354
 
2.1%
59.950001 262
 
1.5%
Other values (6) 264
 
1.5%
ValueCountFrequency (%)
59.919998 3
 
< 0.1%
59.93 16
 
0.1%
59.939999 102
 
0.6%
59.950001 262
 
1.5%
59.959999 577
 
3.4%
59.970001 1090
 
6.3%
59.98 1907
11.1%
59.990002 2813
16.4%
60 3254
18.9%
60.009998 3150
18.3%
ValueCountFrequency (%)
60.07 1
 
< 0.1%
60.060001 26
 
0.2%
60.049999 116
 
0.7%
60.040001 354
 
2.1%
60.029999 1127
 
6.6%
60.02 2383
13.9%
60.009998 3150
18.3%
60 3254
18.9%
59.990002 2813
16.4%
59.98 1907
11.1%

Inlet_Temperature_of_Chilled_Water
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.986345
Minimum11.8
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:55.924242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile12
Q113.6
median15.3
Q316.5
95-th percentile16.9
Maximum17
Range5.2
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation1.5940079
Coefficient of variation (CV)0.10636402
Kurtosis-1.129406
Mean14.986345
Median Absolute Deviation (MAD)1.4
Skewness-0.4234269
Sum257480.4
Variance2.5408611
MonotonicityNot monotonic
2025-03-01T02:44:56.133360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.799999 1442
 
8.4%
16.9 909
 
5.3%
16.700001 751
 
4.4%
16.4 640
 
3.7%
16.1 617
 
3.6%
16.6 596
 
3.5%
13.1 571
 
3.3%
11.9 479
 
2.8%
16.200001 474
 
2.8%
15.8 459
 
2.7%
Other values (43) 10243
59.6%
ValueCountFrequency (%)
11.8 240
1.4%
11.9 479
2.8%
12 147
 
0.9%
12.1 139
 
0.8%
12.2 199
1.2%
12.3 118
 
0.7%
12.4 132
 
0.8%
12.5 176
 
1.0%
12.6 115
 
0.7%
12.7 126
 
0.7%
ValueCountFrequency (%)
17 307
 
1.8%
16.9 909
5.3%
16.799999 1442
8.4%
16.700001 751
4.4%
16.6 596
3.5%
16.5 424
 
2.5%
16.4 640
3.7%
16.299999 342
 
2.0%
16.200001 474
 
2.8%
16.1 617
3.6%

Outlet_Temperature
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.450864
Minimum13.6
Maximum16.799999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-03-01T02:44:56.310584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.6
5-th percentile13.8
Q114.6
median15.5
Q316.4
95-th percentile16.6
Maximum16.799999
Range3.199999
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation0.93656796
Coefficient of variation (CV)0.060615894
Kurtosis-1.1567129
Mean15.450864
Median Absolute Deviation (MAD)0.9
Skewness-0.34647068
Sum265461.3
Variance0.87715955
MonotonicityNot monotonic
2025-03-01T02:44:56.491363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
16.5 1456
 
8.5%
16.4 1278
 
7.4%
14.5 1051
 
6.1%
16.6 959
 
5.6%
16.1 781
 
4.5%
16.700001 726
 
4.2%
16.299999 667
 
3.9%
15.8 661
 
3.8%
13.7 654
 
3.8%
15.4 620
 
3.6%
Other values (23) 8328
48.5%
ValueCountFrequency (%)
13.6 147
 
0.9%
13.7 654
3.8%
13.8 494
2.9%
13.9 187
 
1.1%
14 174
 
1.0%
14.1 294
 
1.7%
14.2 215
 
1.3%
14.3 213
 
1.2%
14.4 572
3.3%
14.5 1051
6.1%
ValueCountFrequency (%)
16.799999 34
 
0.2%
16.700001 726
4.2%
16.6 959
5.6%
16.5 1456
8.5%
16.4 1278
7.4%
16.299999 667
3.9%
16.200001 513
 
3.0%
16.1 781
4.5%
16 433
 
2.5%
15.9 430
 
2.5%

Interactions

2025-03-01T02:44:46.306355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:13.401906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:16.893400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.180773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.188385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.404629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.807745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:29.803970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:33.175023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.482024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.639760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:40.296288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:42.870552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:46.506157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:13.647932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:17.180755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.330750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.342460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.604264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.994755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:30.108955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:33.353216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.670378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.813623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:40.463913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:43.080635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:46.724671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:13.951534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:17.387816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.461487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.517801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.773282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:27.136383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:30.392377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:33.530311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.818416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.973580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:40.637592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:43.271275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:46.939290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:14.209711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:17.550719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.598212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.681096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.954440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:27.279878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:30.572123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:33.696911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.965628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:38.136701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:40.811316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:43.543028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.119905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:14.453463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:17.727102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.761252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.853527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:25.131952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:27.468531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:30.801591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:33.863724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.123163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:38.305140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:40.980966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:43.813803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.299665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:14.734247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:17.890251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.919725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.020425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:25.318945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:27.733590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:31.104225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.038063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.295083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:38.487012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:41.163922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:44.083709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.450960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:14.962706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.028763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.052922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.167000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:25.500369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:27.970593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:31.309376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.195079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.446670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:38.669586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:41.371688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:44.369992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.631127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:15.213663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.184754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.205553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.325773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:25.696556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:28.274760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:31.602025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.368975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.638267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:38.849243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:41.556775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:44.666266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.799333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:15.480211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.340797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.363391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.503393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:25.900775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:28.557905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:32.235958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.580261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.802071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:39.027580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:41.757504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:44.965121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:47.972166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:15.730641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.514133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.535801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.669055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.095132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:28.835580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:32.411070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.751701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:36.953422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:39.202215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:41.934107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:45.244561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:48.166826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:16.019116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.717853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.716552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:22.853249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.277116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:29.074305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:32.616141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:34.938618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.119814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:39.373857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:42.140089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:45.466663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:48.348845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:16.315303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:18.881048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:20.890919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.072375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.451789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:29.349034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:32.812978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.122613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.285388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:39.562759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:42.361333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:45.756733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:48.518499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:16.588356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:19.029905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:21.038171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:24.244429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:26.631610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:29.539099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:32.996139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:35.291577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:37.448635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:39.755840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:42.638075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T02:44:46.002781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-01T02:44:56.652977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Battery_Active_PowerFC_Active_PowerGE_Active_PowerGE_Body_Active_PowerGE_Body_Active_Power_Set_ResponseInlet_Temperature_of_Chilled_WaterIsland_mode_MCCB_AC_VoltageIsland_mode_MCCB_Active_PowerIsland_mode_MCCB_FrequencyMG-LV-MSB_AC_VoltageMG-LV-MSB_FrequencyOutlet_TemperaturePVPCS_Active_PowerReceiving_Point_AC_Voltage
Battery_Active_Power1.0000.028-0.023-0.036-0.049-0.132-0.068-0.027-0.015-0.067-0.012-0.1310.109-0.067
FC_Active_Power0.0281.0000.0830.0950.1160.2000.4380.2310.0570.4040.0570.1510.2920.391
GE_Active_Power-0.0230.0831.0000.5840.7080.2800.017-0.530-0.0410.023-0.0460.264-0.019-0.087
GE_Body_Active_Power-0.0360.0950.5841.0000.7720.2870.006-0.622-0.0990.014-0.1090.2690.017-0.114
GE_Body_Active_Power_Set_Response-0.0490.1160.7080.7721.0000.374-0.008-0.7330.047-0.0030.0410.3520.037-0.151
Inlet_Temperature_of_Chilled_Water-0.1320.2000.2800.2870.3741.0000.036-0.0190.0710.0370.0720.996-0.3770.018
Island_mode_MCCB_AC_Voltage-0.0680.4380.0170.006-0.0080.0361.0000.324-0.0490.990-0.0490.055-0.6230.973
Island_mode_MCCB_Active_Power-0.0270.231-0.530-0.622-0.733-0.0190.3241.0000.0160.3250.0200.012-0.5230.471
Island_mode_MCCB_Frequency-0.0150.057-0.041-0.0990.0470.071-0.0490.0161.000-0.0510.9260.0760.011-0.049
MG-LV-MSB_AC_Voltage-0.0670.4040.0230.014-0.0030.0370.9900.325-0.0511.000-0.0500.057-0.6230.973
MG-LV-MSB_Frequency-0.0120.057-0.046-0.1090.0410.072-0.0490.0200.926-0.0501.0000.0770.009-0.047
Outlet_Temperature-0.1310.1510.2640.2690.3520.9960.0550.0120.0760.0570.0771.000-0.3960.042
PVPCS_Active_Power0.1090.292-0.0190.0170.037-0.377-0.623-0.5230.011-0.6230.009-0.3961.000-0.660
Receiving_Point_AC_Voltage-0.0670.391-0.087-0.114-0.1510.0180.9730.471-0.0490.973-0.0470.042-0.6601.000

Missing values

2025-03-01T02:44:48.789877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-01T02:44:49.092664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-01T02:44:49.534480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TimestampBattery_Active_PowerBattery_Active_Power_Set_ResponsePVPCS_Active_PowerGE_Body_Active_PowerGE_Active_PowerGE_Body_Active_Power_Set_ResponseFC_Active_Power_FC_END_SetFC_Active_PowerFC_Active_Power_FC_end_Set_ResponseIsland_mode_MCCB_Active_PowerMG-LV-MSB_AC_VoltageReceiving_Point_AC_VoltageIsland_mode_MCCB_AC_VoltageIsland_mode_MCCB_FrequencyMG-LV-MSB_FrequencyInlet_Temperature_of_Chilled_WaterOutlet_Temperature
02023/04/01 00:00:01-0.10.00.0110.087.000000122.040.038.040.0-123.0488.0486.0488.060.04000160.04000115.115.5
12023/04/01 00:00:11-0.30.00.0118.0120.000000122.040.038.040.0-87.0488.0486.0488.060.04000160.04000115.115.5
22023/04/01 00:00:210.00.00.0116.0124.000000122.040.038.040.0-116.0488.0486.0488.060.04000160.04000115.115.5
32023/04/01 00:00:31-0.10.00.0110.094.300003122.040.038.040.0-115.0488.0486.0488.060.04999960.04999915.115.5
42023/04/01 00:00:410.00.00.0116.0116.000000122.040.038.040.0-128.0488.0486.0488.060.04999960.04999915.115.5
52023/04/01 00:00:510.00.00.0115.0109.800003122.040.038.040.0-109.0488.0486.0488.060.04000160.04000115.115.5
62023/04/01 00:01:010.00.00.0114.096.000000122.040.038.040.0-115.0488.0486.0488.060.04000160.02999915.115.5
72023/04/01 00:01:110.10.00.078.0133.500000122.040.038.040.0-98.0487.0486.0487.060.04000160.06000115.115.6
82023/04/01 00:01:21-0.10.00.0124.0121.500000122.040.038.040.0-125.0488.0486.0488.060.04999960.04999915.115.5
92023/04/01 00:01:31-0.30.00.0113.0115.500000122.040.038.040.0-119.0487.0485.0488.060.04000160.02999915.115.5
TimestampBattery_Active_PowerBattery_Active_Power_Set_ResponsePVPCS_Active_PowerGE_Body_Active_PowerGE_Active_PowerGE_Body_Active_Power_Set_ResponseFC_Active_Power_FC_END_SetFC_Active_PowerFC_Active_Power_FC_end_Set_ResponseIsland_mode_MCCB_Active_PowerMG-LV-MSB_AC_VoltageReceiving_Point_AC_VoltageIsland_mode_MCCB_AC_VoltageIsland_mode_MCCB_FrequencyMG-LV-MSB_FrequencyInlet_Temperature_of_Chilled_WaterOutlet_Temperature
171722023/04/02 23:42:010.00.00.0180.0175.300003200.040.038.040.0-204.0489.0486.0489.060.02999960.02000015.715.8
171732023/04/02 23:42:11-0.30.00.0223.0168.300003200.040.038.040.0-209.0489.0486.0489.060.02000060.02999915.715.8
171742023/04/02 23:42:21-0.30.00.0208.0206.300003200.040.038.040.0-214.0489.0486.0489.060.02999960.02999915.715.8
171752023/04/02 23:42:31-0.20.00.0198.0197.500000200.040.038.040.0-207.0489.0486.0489.060.02999960.02999915.715.8
171762023/04/02 23:42:41-0.10.00.0197.0193.300003200.040.038.040.0-203.0489.0486.0489.060.02999960.02999915.715.8
171772023/04/02 23:42:510.10.00.0168.0184.300003200.040.038.040.0-165.0489.0486.0489.060.02999960.02999915.715.8
171782023/04/02 23:43:01-0.20.00.0202.0220.500000200.040.038.040.0-187.0489.0486.0489.060.02999960.02000015.715.7
171792023/04/02 23:43:110.00.00.0205.0211.300003200.040.038.040.0-208.0489.0486.0489.060.02999960.02999915.715.8
171802023/04/02 23:43:21-0.10.00.0203.0220.500000200.040.038.040.0-219.0489.0486.0489.060.02999960.02999915.715.7
171812023/04/NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN